Is it necessary to know the details behind the AI algorithms and models?
This is a good question. I tend to think the answer is yes it is necessary to know the details, because a person without mathematical understanding of these algorithms cannot consistently make a model as good as someone who does have that understanding.

The reason is right at the core of computer science: abstractions are useful, but usually obscure details. When those details matter, someone who only knows the abstraction and not the details that lie beneath can't understand what's going on.

As an example, if you don't understand the math behind optimizing the weights of a neural network, it might not be apparent how parameters like the learning rate are impacted by properties like network depth when some of the inputs have not been properly normalized. If you understand the optimization process mathematically, you can reason through the effects even if you are trying to work on an unfamiliar problem. This ability to reason through the probable effects of parameter decisions in new domains is the main thing that you miss by working from intuition.